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Authors: Maria Carmela Groccia 1 ; Rosita Guido 1 ; Domenico Conforti 1 and Angela Sciacqua 2

Affiliations: 1 Department of Mechanical, Energy and Management Engineering, University of Calabria, Ponte Pietro Bucci 41C, 87036 Rende (Cosenza), Italy ; 2 Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Italy

Keyword(s): Predictive Models, Knowledge Discovery, Machine Learning, Chronic Heart Failure, Decision Tree, Respiratory Rate.

Abstract: In this paper, a Knowledge Discovery task has been implemented with the aim of developing models for predicting cardiovascular worsening events in Chronic Heart Failure (CHF) patients. A set of patients suffering from CHF were enrolled and carefully evaluated through a five-year follow-up. Several predictive models were developed on the collected data and then compared. Among these, the decision tree based predictive model has been analysed by clinical experts. The decision tree is among all the trained and tested models the most simple and interpretable mainly by clinicians because it discovers if-then rules. The extracted rules are compliant with previous clinical studies. Nevertheless, the decision tree achieved lower performance compared to the other predictive models, which conversely to the decision tree are not “clinician friendly” because they do not provide an explanation of the classification decisions.

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Paper citation in several formats:
Groccia, M.; Guido, R.; Conforti, D. and Sciacqua, A. (2022). Predictive Tools to Evaluate Cardiovascular Events in Chronic Heart Failure Patients. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 475-481. DOI: 10.5220/0010829900003123

@conference{healthinf22,
author={Maria Carmela Groccia. and Rosita Guido. and Domenico Conforti. and Angela Sciacqua.},
title={Predictive Tools to Evaluate Cardiovascular Events in Chronic Heart Failure Patients},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF},
year={2022},
pages={475-481},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010829900003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - HEALTHINF
TI - Predictive Tools to Evaluate Cardiovascular Events in Chronic Heart Failure Patients
SN - 978-989-758-552-4
IS - 2184-4305
AU - Groccia, M.
AU - Guido, R.
AU - Conforti, D.
AU - Sciacqua, A.
PY - 2022
SP - 475
EP - 481
DO - 10.5220/0010829900003123
PB - SciTePress

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